Alterations in the gut microbiome and its metabolites are associated with the immune response to mucosal immunization with Lactiplantibacillus plantarum-displaying recombinant SARS-CoV-2 spike epitopes in mice

Abstract

Lactic acid bacteria (LAB) expressing foreign antigens have great potential as mucosal vaccines. Our previous study reported that recombinant Lactiplantibacillus plantarum SK156 displaying SARS-CoV-2 spike S1 epitopes elicited humoral and cell-mediated immune responses in mice. Here, we further examined the effect of the LAB-based mucosal vaccine on gut microbiome composition and function, and gut microbiota-derived metabolites. Forty-nine (49) female BALB/c mice were orally administered L. plantarum SK156-displaying SARS-CoV-2 spike S1 epitopes thrice (at 14-day intervals). Mucosal immunization considerably altered the gut microbiome of mice by enriching the abundance of beneficial gut bacteria, such as Muribaculaceae, Mucispirillum, Ruminococcaceae, Alistipes, Roseburia, and Clostridia vadinBB60. Moreover, the predicted function of the gut microbiome showed increased metabolic pathways for amino acids, energy, carbohydrates, cofactors, and vitamins. The fecal concentration of short-chain fatty acids, especially butyrate, was also altered by mucosal immunization. Notably, alterations in gut microbiome composition, function, and butyrate levels were positively associated with the immune response to the vaccine. Our results suggest that the gut microbiome and its metabolites may have influenced the immunogenicity of the LAB-based SARS-CoV-2 vaccine.

Keywords: COVID-19; SARS-CoV-2; butyrate; gut microbiome; lactic acid bacteria; mucosal vaccine.

Figure 1

Changes in the gut microbiome after immunization with LAB-based mucosal vaccine. (A) Changes in alpha diversity: Chao1, Shannon, and Pielou's evenness. (B) Principal coordinate analysis (PCoA) showing Bray–Curtis distance matrix and weight uniFrac. Relative abundance of the gut microbiome at phylum (C) and genus (D) levels. (E) Taxonomic marker identification using Linear Differential Analysis Effect Size (LEfSe). Association between the antibody (IgA and IgG) response with species diversity (F) and Firmicutes:Bacteroidota ratio (G). Association between antibody response and cytokine levels (IL-4, IL-10, IFN-γ) with taxonomic markers (H). Significant differences (alpha diversity) were calculated using Kruskal–Wallis with post hoc Dunn's multiple comparisons tests. Correlation (rho) and p values were calculated using Pearson's correlation with false discovery rate (FDR). P values are denoted by *, **, and ***, at p < 0.05, p < 0.01, and p < 0.001, respectively. ns is not significant.

Figure 2

Changes in fecal short chain fatty acid (SCFA) concentrations after immunization with LAB-based mucosal vaccine. (A) Fecal concentrations of acetate, propionate, and butyrate. Lowercase letters indicate significant differences between groups. (B) Association between SCFAs and taxonomic markers. (C) Association between butyrate and gut microbiota. (D) Association of butyrate levels with antibody (IgA and IgG) and cytokine levels (IL-4, IL-10, IFN-γ). Significant differences were calculated using Kruskal–Wallis with post hoc Dunn's multiple comparisons tests. Correlation (rho) and p values were calculated using Pearson's correlation with false discovery rate (FDR). P values are denoted by *, **, and ***, at p < 0.05, p < 0.01, and p < 0.001, respectively.

Figure 3

Inference of the gut microbiome function using PICRUSt2 and Kyoto Encyclopedia of Genes and Genomes (KEGG). (A) Abundance (log10) of KEGG pathways at level 2. (B) Association of altered KEGG level 2 pathways with the taxonomic markers and antibody-specific IgA and IgG response. (C) Functional marker identification using Linear Differential Analysis Effect Size (LEfSe). Association among the identified functional markers, taxonomic markers, and antibody (IgA and IgG) response. (D) Association of altered KEGG level 3 pathways with the taxonomic markers and antibody (IgA and IgG) response. Correlation (rho) and p values were calculated using Pearson’s correlation with false discovery rate (FDR). P values are denoted by *, **, and ***, at p < 0.05, p < 0.01, and p < 0.001, respectively. ns, not significant.